Tutorial
1 Overview
The following section provides an example of possible workflow. It is important to note that these are indeed examples of the software’s capabilities and are not intended to be used as scientific advice in a spatial conservation planning process. It is the user’s responsibility to ensure that all analysis decisions are valid.
library(sf)
library(leaflet)
library(tmap)
library(tidyverse)
library(DT)
# set default projection for leaflet
proj <- "+proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null +wktext +no_defs"1.1 Landscape Data using CSM
Download the example project folder. This folder contains the Marxan Connect Project file, the input data, and the output data from this example. Feel free to follow along using Marxan Connect by loading tutorial.MarCon.
Before adding connectivity to the mix, let’s have a look at the ‘traditional’ Marxan files. The files include hexagonal planning units that cover the Great Barrier Reef and we’ve identified a few bioregion types for which we’ve set conservation targets.
1.1.1 spec.dat
spec <- read.csv("tutorial/CSM_landscape/input/spec.dat")
datatable(spec,rownames = FALSE, options = list(searching = FALSE))1.1.2 puvspr.dat
The table shown here is a trimmed version showing the first 30 rows as an example of the type of data in the puvspr.dat file. The original dataset has 974 entries.
puvspr <- read.csv("tutorial/CSM_landscape/input/puvspr.dat")
datatable(puvspr[1:30,],rownames = FALSE, options = list(searching = FALSE))1.1.3 pu.dat
The table shown here is a trimmed version showing the first 30 rows as an example of the type of data in the puvspr.dat file. The original dataset has 653 entries.
pu <- read.csv("tutorial/CSM_landscape/input/pu.dat")
datatable(pu[1:30,],rownames = FALSE, options = list(searching = FALSE))1.1.4 Inital Conservation Features
This map shows the bioregions, which serve as conservation features in the Marxan analysis with no connectivity.
puvspr_wide <- puvspr %>%
left_join(select(spec,"id","name"),
by=c("species"="id")) %>%
select(-species) %>%
spread(key="name",value="amount")
# planning units with output
output <- read.csv("tutorial/CSM_landscape/output/pu_no_connect.csv") %>%
mutate(geometry=st_as_sfc(geometry,"+proj=longlat +datum=WGS84"),
best_solution = as.logical(best_solution)) %>%
st_as_sf() %>%
left_join(puvspr_wide,by=c("FID"="pu"))map <- leaflet(output) %>%
addTiles()
groups <- names(select(output,-best_solution,-select_freq))[c(-1,-2)]
groups <- groups[groups!="geometry"]
for(i in groups){
z <- unlist(data.frame(output)[i])
if(is.numeric(z)){
pal <- colorBin("YlOrRd", domain = z)
}else{
pal <- colorFactor("YlOrRd", domain = z)
}
map = map %>%
addPolygons(fillColor = ~pal(z),
fillOpacity = 0.6,
weight=0.5,
color="white",
group=i,
label = as.character(z)) %>%
addLegend(pal = pal,
values = z,
title = i,
group = i,
position="bottomleft")
}
map <- map %>%
addLayersControl(overlayGroups = groups,
options = layersControlOptions(collapsed = FALSE))
for(i in groups){
map <- map %>% hideGroup(i)
}
map %>%
showGroup("BIORE_102")1.1.5 Adding Connectivity
Let’s begin by examining the spatial layers we’ve added in order to incorporate connectivity into the Marxan analysis. Marxan Connect needs a shapefile for the planning units, the focus areas, and the avoidance areas. These spatial layers are shown in the map below.
# planning units
pu <- st_read("tutorial/CSM_landscape/hex_planning_units.shp") %>%
st_transform(proj)
#focus areas (IUCN level I or II protected areas)
fa <- st_read("tutorial/CSM_landscape/IUCN_IorII.shp") %>%
st_transform(proj)
# avoidance areas (ports)
aa <- st_read("tutorial/CSM_landscape/ports.shp") %>%
st_transform(proj)p <- qtm(pu,fill = '#7570b3') +
qtm(fa,fill = '#1b9e77') +
qtm(aa,fill = '#d95f02')
tmap_leaflet(p) %>%
addLegend(position = "topright",
labels = c("Planning Units","Focus Areas (IUCN I or II)","Avoidance Areas (ports)"),
colors = c("#7570b3","#1b9e77","#d95f02"),
title = "Layers")1.1.6 connectivity_matrix.csv
The connectivity data is at the ‘heart’ of Marxan Connect’s functionality. It allows the generation of new conservation features based on connectivity metrics.
Below is the connectivity matrix of our example conservation priority. For the sake of you web browser, this table only contains the 7 row and columns of the connectivity matrix. The real file has 1279227 entries.
conmat <- read.csv("tutorial/CSM_landscape/IsolationByDistance.csv")
datatable(conmat[1:7,],rownames = FALSE, options = list(searching = FALSE))1.1.7 boundary.dat
This table represents the boundaries between planning units as the values in the connectivity matrix above.
The table shown here is a trimmed version showing the first 30 rows as an example of the type of data in the boundary.dat file. The original dataset has 38648 entries.
boundary <- read.csv("tutorial/CSM_landscape/input/boundary.dat")
datatable(boundary[1:30,],rownames = FALSE, options = list(searching = FALSE))Finally, running Marxan with the connectivity conservation features and boundary definitions results in a different solution.
# planning units with output
output <- read.csv("tutorial/CSM_landscape/output/pu_connect.csv") %>%
mutate(geometry=st_as_sfc(geometry,"+proj=longlat +datum=WGS84"),
best_solution = as.logical(best_solution),
# fa_included = as.logical(gsub("True",TRUE,.$fa_included)),
# aa_included = as.logical(gsub("True",TRUE,.$aa_included))
) %>%
st_as_sf()map <- leaflet(output) %>%
addTiles()
groups <- names(output)[c(-1,-2,-length(names(output)))]
for(i in groups){
z <- unlist(data.frame(output)[i])
if(is.numeric(z)){
pal <- colorBin("YlOrRd", domain = z)
}else{
pal <- colorFactor("YlOrRd", domain = z)
}
map = map %>%
addPolygons(fillColor = ~pal(z),
fillOpacity = 0.6,
weight=0.5,
color="white",
group=i,
label = as.character(z)) %>%
addLegend(pal = pal,
values = z,
title = i,
group = i,
position="bottomleft")
}
map <- map %>%
addLayersControl(overlayGroups = groups,
options = layersControlOptions(collapsed = FALSE))
for(i in groups){
map <- map %>% hideGroup(i)
}
map %>%
showGroup("select_freq")Here is the output of our example with no connectivity for comparison.
# planning units with output
output <- read.csv("tutorial/CSM_landscape/output/pu_no_connect.csv") %>%
mutate(geometry=st_as_sfc(geometry,"+proj=longlat +datum=WGS84"),
best_solution = as.logical(best_solution),
# fa_included = as.logical(gsub("True",TRUE,.$fa_included)),
# aa_included = as.logical(gsub("True",TRUE,.$aa_included))
) %>%
st_as_sf()map <- leaflet(output) %>%
addTiles()
groups <- names(output)[c(-1,-2,-length(names(output)))]
for(i in groups){
z <- unlist(data.frame(output)[i])
if(is.numeric(z)){
pal <- colorBin("YlOrRd", domain = z)
}else{
pal <- colorFactor("YlOrRd", domain = z)
}
map = map %>%
addPolygons(fillColor = ~pal(z),
fillOpacity = 0.6,
weight=0.5,
color="white",
group=i,
label = as.character(z)) %>%
addLegend(pal = pal,
values = z,
title = i,
group = i,
position="bottomleft")
}
map <- map %>%
addLayersControl(overlayGroups = groups,
options = layersControlOptions(collapsed = FALSE))
for(i in groups){
map <- map %>% hideGroup(i)
}
map %>%
showGroup("select_freq")# habitats <- st_read('../data/shapefiles/habitat.shp') %>%
# st_transform(proj)# p <- tm_shape(habitats) +
# tm_fill("habitat",title="Habitat Type")
# tmap_leaflet(p)